1. Field of the Invention
The present invention relates to processing of digital images. More particularly, the present invention relates to methods for suppressing structured noise such as row and column patterns in a digital image.
2. The Prior Art
Structured noise in digital images can have the form of row and column patterns, typically produced in a digital camera due to the sensor design (e.g., signal readout mode), or various other artifacts introduced to the image via image processing.
Row and column patterns are usually more obvious in images taken at higher ISO due to applying a gain to the image. Patterns are usually much more visible in images generated using refined processing aimed at high resolution output than images passed through aggressive denoising and JPEG compression. Therefore, software correction is needed to produce acceptable image quality in applications aimed at high resolution output. The problem is quite complex; the visibility of patterns usually varies with the scene content. In the present invention, row and column patterns (together with noise and edge/detail information) are extracted from the original image using a so-called residual signal. In one example, the residual signal is a three-channel color image. In another example, the residual signal is a one-channel image, allowing faster and simpler processing than detecting and correcting the patterns in RGB image data. Traditional filtering methods used to smooth the image reduce overall image sharpness and resolution of the residual image. Both of these effects are unacceptable.
The most common way of suppressing structured noise in digital images is to apply a smoothing technique, usually with a large spatial (two-dimensional) kernel. Unfortunately, such techniques tend to suppress desired image features such as edges and image details. Moreover, due to the required large spatial support it is also common that such techniques are slow, computationally complex, and have high memory requirements.
In the case of row and column patterns, it is also possible to use a smoothing solution which applies one-dimensional kernels in the direction orthogonal to the structure to be suppressed (i.e., row patterns are suppressed via smoothing in the vertical direction and column patterns suppressed via smoothing in the horizontal direction). However, such techniques often produce unsatisfactory results as they cannot fully remove structured noise and also tend to suppress the desired image features such as edges and image details. Therefore, a different approach is needed.